1. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
The ecoRacer game interface (a)–(d) and settings (e). The game interface (a) is designed to work for computer and mobile screens. Touching the gear icon to the right enters the design screen (b), where the final drive ratio can be tuned. After each play, the score board (c) is shown, and the player can review speed and motor efficiency profiles of the current play in (d). Vehicle specifications, motor efficiency map, and maximum torque curves are shown in (e).

2. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
DP results for the optimal final drive ratio. Top to bottom: Road profile, optimal speed profile corresponding to ρ*=18, and optimal control decisions in terms of braking and acceleration.

3. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
A one-dimensional example of EGO search (a)–(d). At each iteration, the algorithm updates the kriging model (in solid line) and chooses the next sample (triangle) according to the maximum expected improvement (merit) function.

4. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
The “learned” subspace of solutions provides more rational control strategies. (a) Percentage of test points that follow rules (A) and (B) for a variety of states. The percentage is calculated for all uniformly sampled solutions in S, and for those that satisfy ϕ>0. (b) A summary of tested states and percentages averaged across all state values.

5. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
Statistics of the ecoRacer game. (a) Distribution of players by the number of games they played, and distribution of plays by their score. (b) Performance comparison of human player and the EGO algorithm for the first 200 plays.

6. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
Comparison between EGO with and without human knowledge, using the inverse, hill, zigzag and long tracks. The long track is five times as long as the others.

7. Date of download: 11/6/2017
Copyright © ASME. All rights reserved.
From: EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
J. Mech. Des. 2016;138(6): doi: /
Figure Legend:
EGO performance on the long track with constraints from (1) all human plays (2) all EGO plays, (3) the first 500 human plays, and (4) the first 500 EGO plays